Free satellite image data application for monitoring land use cover changes in the kon ha nung plateau, vietnam

Remote sensing imagery is the most suitable tool for monitoring, managing, and evaluating land-use overlay fluctuations, especially forest cover for large areas. Free- and medium-resolution satellite imagery is a useful tool that allows scientific researchers and management organizations to monitor forest development in developing countries, such as Vietnam. In this study, we used SPOT 4 and Planet remote sensing data to assess land-use status fluctuations in the Kon Ha Nung Plateau area, Vietnam, between 2000 and 2021 (the overall accuracy was 90.52%, Kappa value = 0.89). The results showed that from 2000 to 2010, the rate of natural forest loss in this area was 0.32%/year, of which, more than 6500 ha were converted to other uses. Between 2010 and 2021, the rate of natural forest loss gradually decreased (0.09%/year) instead of fluctuating between different types of land use. The area of forests, perennial crop land, and annual crop land tended to increase from 2000 to 2010; however, from 2010 to 2021, the area of plantation forests decreased markedly, while the area of perennial crop land and annual crop land continued to expand. The analysis of the policies on forest management, exploitation, and protection was applied locally, to explain the causes of the change in spatiotemporal aspects of the types of land-use cover in the Kon Ha Nung Plateau. Restoring forest areas during 2010–2021 initially improved effectiveness in forest management and protection. Furthermore, the results provide a better understanding of the current position and role of the government apparatus, cadres, and ethnic minorities in socioeconomic development associated with forest protection and development on the Kon Ha Nung Plateau. The results of this study can help managers monitor annual forest-cover fluctuations based on free remote sensing imagery to reduce both the cost of management and surveying, yielding relatively accurate results.


Introduction
Forests and forest ecosystems cover a large part of the Earth and account for the majority of biological productivity on continents. The structure and function of forests are regulated by a wide range of factors, including the physical environment, plant growth, Vietnam.
The Kon Ha Nung Plateau in the Gia Lai Province, Vietnam, was registered as the World Biosphere Reserve at the 33rd session of the UNESCO International Coordinating Council of the Human and Biosphere Program (MAB-ICC) in Abuja, Nigeria, on September 15, 2021. The core areas of this biosphere reserve are the Kon Ka Kinh National Park and Kon Chu Rang Nature Reserve. The Kon Ka Kinh National Park and Kon Chu Rang Industrial Park are relatively intact forests that are highly diverse in vegetation characteristic types: tropical enclosed forests evergreen broad-leafed; coniferous evergreen rainforest; evergreen forests with broad leaves; coniferous evergreen forests characteristic of the forest ecosystems of the Central Highlands provinces, with many unique features and outstanding and unique characteristics [52]. Additionally, a carpet of shrubs, grasslands, agricultural land, and residential areas are present. In the period of 2000-2021, the changes in forest cover were mainly related to the forest conservation and developmental policies of the Government of Vietnam as well as the local authorities.
The objective of this study was to map forest-cover fluctuations for the period of 2000-2021 based on remote sensing images (GIS), impact analysis, assessment of the effectiveness of policies for protection, forest development, and changes in land-use structure in the Kon Ha Nung Plateau area in Vietnam.

Study area
Kon Ha Nung Plateau is a plateau in Gia Lai province, spread over the area of K'bang and An Khe districts, and was recognised by UNESCO as a world biosphere reserve on September 9, 2021. Kon Ha Nung Plateau Biosphere Reserve has a total area of more than 413,500 ha. The core area of this biosphere reserve is Kon Ka Kinh National Park and Kon Chu Rang Nature Reserve. The buffer zone is a forest area stretching from Chu Pȃh district to Kbang district (Gia Lai), bordering Kon Ray district, Kon Plong (Kon Tum province), Ba To district (Quang Ngai province), An Lao district, and Vinh Thanh (Binh Dinh province) (Fig. 1).
The plant ecosystem here is mainly a type of moist tropical evergreen forest in low and medium mountains [53]. Part of the area is a semi-evergreen forest or picket forest. The flora here is highly biodiverse, rated A for international importance, with thousands of species. In particular, there are many rare plant species included in the Red Book of Vietnam and the world such as Hopea hainanensis, Aquilaria crassna, Anoectochilus setaceus, Dalbergia cochinchinensis, Pterocarpus macrocarpus Kurz, etc. The animal ecosystem here also has thousands of species. Many species are in the group that should be preserved in the Red Book of the world such as Pygathrix cinerea, Nomascus annamensis, Buceros bicornis, Heliopais personatus, Pterocarpus macrocarpus, Aquilaria crassna, etc [54].
In the buffer zone of the Kon Ha Nung Plateau, there are many ethnic minority communities, mainly Bahnar and Jrai people. These are communities with unique cultures; in particular, their lives are associated with the Central Highlands Gongs Cultural Space, which is inscribed by UNESCO on the list of intangible cultural heritage [54].

Materials
In the Kon Ha Nung Plateau area, January-March is the end of the dry season, with very little rain. Satellite imagery at the study site during this period was virtually unaffected by cloud factors as well as other atmospheric factors, which is consistent with the quality and timing of image acquisition. On the basis of temporal selection when satellite imagery had the lowest cloud cover during this period, we used SPOT-4 satellite imagery data from 2000 (Fig. 2a), 2010 (Fig. 2b), and Planet (2021) (Fig. 2c) to map the land-use cover of the Kon Ha Nung Plateau, Gia Lai Province, Vietnam (Table 1) The image-interpreting key template (MKA) used for 2021 photo prediction was included in the analysis on Collect Earth. Next, 80% of the total number of templates was assigned a status after fragmentation. The remaining 20% of MKA (330 samples) was used to verify the scene and assess the accuracy (Fig. 2d). The same was applied to the 2000 and 2010 MKA sets, which introduced the MKA sets of each year into the Earth software and used Google Earth photos to assign statuses for interpretation.
First, the image lock for image interpretation will be included in the eCognition software to conduct identification according to the image properties. Next, the multiresolution segmentation algorithm will rely on the uniform criteria of the image lock template to merge pixels based on similarity [57]. Here, the similarities between the photo pixels are defined as the spectral characteristics of the photo [58]. The multiresolution segmentation algorithm allows for locating objects and the boundaries between them. In the next step, objects with a small area will be merged into objects with a larger area. At this step of combining small areas, the process was optimised to minimise the heterogeneity of objects based on the 2 parameters n and h (n is the size of a circle, and h is the arbitrary definition of heterogeneity). As a result, adjacent pairs of similar objects will be merged together to form a larger circle. The process stops when the smallest growth exceeds the threshold defined by the rate parameter [59].
Spectral or color heterogeneity was described by the following formula: Accordingly, the heterogeneity of the value h is calculated as the ratio between the circumference of a circle and the square root of the total number of pixels that make up that circle (called n). The calculation formula is described as follows [60]: 2.3.1.2. Categorized by the random forest algorithm. According to Genuer (2020), the method of assigning status after image segmentation performed by eCognition Developer software using random forest is a newly developed technique. The random forest classification method, which is a comprehensive machine learning method for classification, regression, and other tasks that functions by building many decision trees during the training period and providing layers that are the method of the layers (classification) or the average prediction (regression) of each tree [61]. Accordingly, the random forest algorithm was described as follows [50].
Consider an ensemble of classifiers h1(X) and h2(X), h k (X), and with the training set drawn at random from the distribution of the random vector Y, X, we define the margin function as: where, I (⋅) is the indicator function. The margin measures the extent to which the average number of votes at X and Y for the correct class exceeds the average vote for any other class. The larger the margin, the greater is the confidence in the classification. The generalization error is given by: where, the subscripts X and Y indicate that the probability is greater than that in the X and Y spaces.

Verification of classification results
Checking the accuracy of the image interpretation results is the next step after the preliminary image guessing results have been obtained. At this step, each subject needs a minimum of 10 points to verify the accuracy of the field survey results. If the accuracy is less than 75%, it is necessary to re-check the image interpretation process and redefine the image interpretation key to improve the accuracy of the process [62]. The Kappa value (K) is regarded as the most effective tool for evaluating the accuracy of image interpretation results [63]. Accordingly, to calculate K, it is necessary to build a table of error matrices with the deviation of the matrix based on the data of rows and columns [64] and define the following values.
1. Overall accuracy assessment 2. User accuracy assessment

Producer accuracy assessment
The formula defines the K value as follows: where, r is the number of columns in the image matrix, X ii is the number of pixels observed in row i and column i (on the main diagonal), i receives values from 1 to r, X i+ is the total pixels observed in row i, X +i is the total pixels observed in column i, and N is the total number of pixels observed in the image matrix. The usual K value will range from 0 to 1. For a K value less than 0.4, the accuracy is rated as low; if the K value ranges from 0.4 to 0.8, the accuracy is rated as average; if the K value is greater than 0.8, this is considered high accuracy, according to the U.S. Geological Survey.

Mapping land-use cover fluctuations
To map the fluctuations in land-use cover for the periods 2000-2010 and 2010-2021 in the Kon Ha Nung Plateau area, we first determined the current land-cover attributes for each contour of the vegetation maps for the periods 2000-2010 and 2010-2021. Accordingly, seven types of land covers, namely natural forest, plantation forest, industrial crop land, shrub, grassland, bare land, agricultural land, rural resident land, and water surface were considered.
Second, we overlaid two layers of maps that had homogenized attributes of land cover according to the corresponding years of the vegetation-change map, based on the intersection algorithm of ArcGIS 10.8 software. The overlay of the maps was modeled, as shown in Fig. 3.
Thus, the results created a layer of land-use fluctuation map showing the cover states between the two moments.

Results of image classification and disinterest
Based on the results of the examination of photo guessing samples and formulas for calculating coefficient K (Equation (5)), an overall accuracy index matrix table was established for the current map of land-use cover in the Kon Ha Nung Plateau area in 2021 ( Table 2).
According to Table 2, the overall accuracy index for 2021 is 90.53%, corresponding to K = 0.89. Accordingly, the water surface objects are the most easily recognizable, with a total of 19/20 samples interpreting images in accordance with reality. Perennial cropland and plantation forest area can be misinterpreted, with a total of 5 out of 100 samples of these two subjects having been misinterpreted. The structure of the satellite image of these two subjects has many similarities, resulting in inaccuracies duirng image interpretation. Annual croplands were also observed to suffer from relatively high rates of decoding to shrubs, grasslands, and bare land (4/40 samples). Some natural forest dissection samples were also misinterpreted as plantation forests (2/100 samples) and perennial cropland (2/100 samples). The type of land in rural resident areas also had a high rate of misinterpretation of annual crop land, shrub, grassland, and bare land (2/30 samples misinterpreted).
Overall, all seven land-use types included in the image analysis had an accuracy of over 80%. Natural forests and water surfaces had the highest user accuracy (97.92% and 95.00%, respectively), in which, the map accuracy of the water surface type was the highest of all types (95%). Shrub, grassland, and bare land had the lowest user accuracy (82.22%), while rural resident land had the lowest producer accuracy (83.33%).

Mapping of the status of land-use cover in the kon ha nung plateau area
Based on the process of interpreting temporal remote sensing images with the multiresolution segmentation algorithm (Equation (1  (1) and (2(2)), the random forest algorithm (Equation (3(3) and (4(4)), and evaluating the results of image classification, overlay maps of the Kon Ha Nung Plateau area were established three times: 2000, 2010, and 2021 (Fig. 4).
According to Table 3 In 2000, the proportion of natural forest area in the Kon Ha Nung Plateau area was the highest among all types of land use, accounting for 63% of the total area (Fig. 5). The proportions of annual cropland and shrub, grassland, and bare land ranked 2nd and 3rd, respectively (12% and 10%, respectively). Simultaneously, the area of plantation forests and land in rural residential areas was relatively small. Similar to 2000, the area structure of land-use types in 2010 and 2021 did not change significantly. Particularly, the structure of the natural forest area decreased to 60% in both years, and the area of annual cropland was higher than that in 2000, at 13% for both years. Similarly, the total areas of shrubland, grassland, and bare land also declined compared to 2000, falling to 9% in 2010 and 7% in 2021. The proportion of plantation forests was relatively stable, ranging from ~4 to 5% during 2000-2021. The Table 2 Error matrix to solve the image of land-use cover in the Kon Ha Nung Plateau area.   proportion of rural resident land over the years has remained consistent from 2000 to 2021 and 2021, at 2-3% of the total natural area of the Kon Ha Nung Plateau (Fig. 5).

Assessment of land-use cover fluctuations in the kon ha nung plateau area
Based on the map of the status of land-use cover of three years, i.e., 2000, 2010, and 2021, two maps of land-use cover fluctuations were established, and the fluctuation of the area of the forest cover in the Kon Ha Nung Plateau area through two stages i.e., 2000-2010 and 2010-2021, were assessed.
According to Fig. 6, at the end of the 2000-2010 period, more than 6500 ha of the natural forests in the Kon Ha Nung Plateau area was lost, which was mainly converted into plantation forests, perennial crop lands, and shrubs, grasslands, and bare land (1058.88 ha, 3309.51 ha, and 866.23 ha, respectively). However, nearly 1500 ha of other types of land were converted into natural forest areas, mainly shrubs, grasslands, bare land, annual croplands, and perennial croplands. The area of perennial crop lands was also highly volatile during this period, with more than 1700 ha of its area converted into other types of land use. Particularly, nearly 900 ha of perennial crop land was converted into land for annual crop lands. Contrastingly, natural forest area contributed more than 3300 ha, and over 1000 ha of annual crop land were also converted into perennial crop land. During 2000-2010, the total areas of shrubs, grassland, and bare land also varied, and nearly 4500 ha were converted to other types of land use, mainly annual crop land, plantation forests, and natural forests (2459.12, 1093.95, and 747.80 ha, respectively). More than 1300 ha of natural forests have been converted to shrubs, grasslands, and bare lands. The type of land in the countryside and the water surface did not fluctuate significantly during this period. The strongest transformation of the two types manifested in just over 350 ha of perennial crop land that has been converted into rural resident land.
The land-use types in the Kon Ha Nung Plateau area showed strong fluctuations from 2010 to 2021 (Fig. 7). Particularly, nearly 18,000 ha of natural forests were converted into other types of land use, mainly perennial croplands, annual crop lands or shrubs, grasslands, and bare lands (5949.55, 5467.90, and 4810.81 ha, respectively). However, more than 16,300 ha of other types of land use were converted to natural forest lands, such as 6233.53 ha of shrubs, grasslands, and bare lands, 5067.23 ha of annual crop lands, and 3027.47 ha of plantations. The plantation forest area also drastically changed, mainly being converted to natural forests, annual crop land, perennial crop land, shrubs, grasslands, and bare lands (a total of more than 8600 ha was converted).
Contrastingly, more than 5300 ha of other types of land were converted to plantation forests, mainly annual crop lands, natural forests and shrubs, grasslands, and bare lands. Annual crop land was one type of land use that drastically altered during this period. The   annual croplands, but the largest were perennial crop land, shrubs, grasslands, bare lands, and natural forests.
The type of land in rural residents also tended to increase in area during 2010-2021, which was mainly converted from annual crop land (1824.42 ha). However, some areas of land in the countryside were also converted to shrubs, grasslands, and bare lands. The water surface area also expanded fairly largely, mainly being converted from annual crop land and shrubs, grasslands, and bare lands (increased to more than 1000 ha, concentrated largely in the Ka Nak lake area of the K'bang district) (Fig. 8).

Effect of forest protection policies and development on land-use area fluctuations in the kon ha nung plateau area
During 2000-2021, the government of Vietnam and the Gia Lai People's Committee implemented many policies to strengthen the management and support forest protection in the Central Highlands, Gia Lai Province, and the Kon Ha Nung Plateau area. These policies are stated as follows.
1. Decision No. 07/1999/CT-UB dated May 4, 1999, of the Gia Lai People's Committee on a number of urgent measures to prevent the exploitation, processing, transportation, sale, and use of illegal wood and forest products, which include cutting down forest trees as pillars, destroying forests, encroaching on forestland to grow coffee. Accordingly, for deforested areas, encroachment on forestland to grow coffee or food crops and trees, apart from handling crimes against forest protection and development, it is protection and development, which is associated with the policy of rapid and sustainable poverty reduction and support for ethnic minorities from 2015 to 2020. Accordingly, economic households in difficult mountainous areas and ethnic minorities in the Kon Ha Nung Plateau area are prioritized to carry out tasks related to forest management and protection, receiving forest securities, with a support amount of VND 400,000/ha/year. Additionally, added reforestation support is funded by a budget of VND 1,600,000/ha/year in the first 3 years and VND 600,000/ha/year for the next 3 years. Support of 5,000,000 VND for 10, 000, 000/ha to buy seedlings, fertilizers, and other costs for reforestation. Supported rice at a rate of 15 kg/gun/month or in corresponding money, but not more than 7 years. Additionally, there are a number of other loan support policies to promote the economy for disadvantaged households and ethnic minorities. Additionally, the authorities at all levels and management boards of special-use forests and protection forests in the area of the Kon Ha Nung Plateau implemented propaganda programs for people in the area of forest protection and forest allocation, improving the important role of forest resources for local people, especially ethnic minorities.

Land-use cover maps of the kon ha nung plateau area based on temporal remote sensing imagery
Temporal remote sensing is a suitable tool for monitoring and assessing fluctuations in forest resources and other types of land-use cover [31,65], which is the basis for management, supervision, conservation, and sustainable development of forest resources for managers and local authorities [66].
The method of using high-resolution remote sensing imaging provided accuracy when mapping the land-use cover in the Kon Ha Nung Plateau area (overall accuracy = 90.53%; K = 0.89). This was highly accurate when compared to the results of certain previous studies that used satellite imagery Landsat-8 and Sentinel-2 (overall accuracy = 75%; K = 0.65) [67], those that used an amalgamation of Sentinel-2A, Sentinel-1A, Landsat-8 and Digital Elevation Model imaging data in Wuhan, China, with an overall accuracy of 82.78% [48], or those that used Sentinel-1 and Sentinel-2 satellite imagery in Spain and Brazil (the intermediate K value was only 0.59-0.83 for Sentinel-2 and 0.28-0.72 for Sentinel-1 images) [36]. The mapping accuracy in our study is also higher than that of studies that used low-resolution multitemporal Landsat imagery data for mapping the eastern Sundarbans, Bangladesh, from 1989 to 2019 to understand mangrove dynamics over a period of 30 years; the overall accuracies of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively [23], and in Sundarbans, the use of Landsat satellite imagery between 1975 and 2020 for mangrove cover classification resulted in an accuracy of 84.8-90% [68]. The combination of temporal remote sensing images and topographic data also contributed to increased map accuracy, such as the use of consecutive Landsat satellite imagery in a time series with an overall accuracy of up to 90.52%; whereas combining topographic data improved the overall accuracy by 92.63% in Vinton County, southeastern Ohio [69]. When both WorldView-2 and airborne LiDAR data were used in Toronto, Canada, the overall accuracies obtained using ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively [70].
In this study, we enhanced the mapping accuracy by classifying forests using random forest algorithms. This method significantly enhanced the accuracy of land-cover mapping [47]. Sentinel-2 and Landsat-8 satellite imagery were used for analysis based on forest classification using random forest algorithms that provided better mapping accuracy, as in Brazil, with a K coefficient of ~0.9 [39], which is the same as that of our study. In a study in Gabon, Sentinel-2 satellite imagery combined with random forest taxons resulted in an overall accuracy of 83.4-97.4% [37]. Additionally, this method has been applied to the classification of tree species with a relatively high overall accuracy (84.5%; K value 0.73) [38] and an overall accuracy of 82% in Austria [51].
However, for free satellite images of medium resolution, mapping accuracy still has certain errors despite being used along with the random forest algorithm and the eCognition software. For improving errors and enhancing map accuracy, conducting field surveys and collecting sufficiently large image interpretation keys is necessary to increase the accuracy of the image analysis process. Additionally, medium-resolution carpet images should only be applied for sufficiently large areas and scales that are suitable to the mapping scale.
The discrepancy in the process of interpreting photos of the Kon Ha Nung Plateau area is mainly on the types of annual croplands and other soils, empty land without plants, in accordance with the study by Fekri et al. [71]. These types have certain structural similarities in satellite image data and are prone to misinterpretation. Additionally, interpretive patterns between plantations and perennial crops have a relatively large rate of misinterpretation, as they have many structural similarities in the satellite imagery. For minimizing errors in the interpretation process, including a field process of verifying the image interpretation key sample set is necessary.

Fluctuations and causes of land-use cover fluctuations in the kon ha nung plateau from 2000 to 2021
During 2000-2021, the government and local authorities in Vietnam implemented many policies to strengthen the management, protection, and development of forest resources in the Kon Ha Nung Plateau area. These policies have had specific impacts on the fluctuation of the area of land-use covers in the region, which is typical of the fluctuation of the area of natural forests, plantation forests, annual planting land, and perennial planting land.
During 2000-2010, the areas of all types of plantations, perennial planting land, and annual tree land increased significantly. Decision No. 10/2003/CT-UB on the recovery of illegally encroached areas for industrial and agricultural crops is the cause of the area fluctuation in the types of crop lands and annual crop lands, shrubs, grasslands, and bare lands. Additionally, in the conversion of Ha Nung Forestry to Ha Nung Forestry Company, the deployment of reforestation and industrial trees in the areas of vacant land, and grassland in the management area have contributed to minimizing the area of shrubs, grasslands, and vacant land, instead of perennial planting lands, such as Coffea canephora, Macadamia, citrus plants, and plantation forests. Although there were many policies on forest protection and development, the forest protection work during 2000-2010 were not highly effective. This is reflected in the decrease in forest area during this period, with the rate of forest loss reaching 0.32%/year, equivalent to more than 5000 ha of natural forest being converted into different types of land use.
The forest-loss rate decreased significantly between 2010 and 2021. For achieving the above effect, policies to strengthen forest protection management (decision No. 18/2007/QD-TTg, decision No. 38-CTr/TU) and implementing forest protection securities (decision No. 75/2015/ND-CP) are crucial. Particularly, the policy on forest protection and support for ethnic minority people to stabilize their livelihoods is considered highly suitable for the Kon Ha Nung Plateau area, where up to 80% of the population comprises ethnic minorities. Therefore, the total area of natural forests in the Kon Ha Nung Plateau area was relatively stable during this period. Particularly, the natural forests in two special-use forest areas on the Kon Ha Nung Plateau (Kon Ka Kinh National Park and Kon Chu Rang Nature Reserve) remained stable. This is the result of many policies to reorganize the management apparatus of forest management boards and policies to strengthen the protection of special-use forest resources in the Kon Ha Nung Plateau area. However, the fluctuation of area between natural forests and types of land use in this period is still relatively high and concentrated in the areas of production forests next to villages. Decision No. 75/2015/ND-CP also supports seedlings, fertilizers, and supplies for ethnic minorities, helping to restore areas of shrubs, grasslands, and bare lands in the area into areas of plantation forests and perennial crop lands during 2010-2021.
During 2000-2021, many policies to strengthen the management, protection and development of forests were enforced for the Kon Ha Nung Plateau. These policies have partially reduced the loss of natural forests before the 2000s; however, they have not yet had a clear effect. Among them, Decision No. 75/2015/ND-CP is considered the most important, contributing to stabilizing the livelihoods of ethnic minorities in the locality, linking the management of forest protection with the village community, and supporting people in planting forests to recover forests. This is also the general policy of the government of Vietnam for the management, protection, and sustainable development of forest resources nationwide. The culture of indigenous peoples, their understanding of nature, and their skills to adapt to the environment related to forests make an important contribution to the conservation of natural resources and socioeconomic development associated with the lives of residents in mountainous areas [7].

Conclusions
In this study, current land-use status maps and land-use overlay fluctuation assessments based on free medium resolution remote sensing imagery were established for the Kon Ha Nung Plateau area for the period 2000-2021. We propose that free remote sensing images of medium resolution may be used to monitor annual land-use fluctuations on large-scale areas.
Our study has a certain limitation. Medium-resolution remote sensing images are only suitable for large-scaled areas, such as districts, provinces or regions that are even larger. For small areas, even for areas approximately 100-200 ha, relatively higher resolution remote sensing imaging data are needed. Drones may be used for monitoring land-use mantle fluctuations.
Our study results may provide a basis for formulating policies to protect and develop forest resources appropriately and for planning the development of local forestry industry in a timely and effective manner. In the future, with the advancement of remote sensing technology, increased remote sensing image resolution can be attained, thereby providing an effective tool for managers to monitor annual land-use overlay fluctuations with increased accuracy.

Declaration of competing interest
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